Constraint-Following Approach for Platoon Control Strategy of Connected Autonomous Vehicles
Bibliographic record
Abstract
The current platoon control strategies of connected autonomous vehicles (CAVs) focus on controlling the fixed intervehicle distance, i.e., the string stability of the platoon system. Here, we aimed to design a CAV platoon control strategy based on a constraint-following approach to solve the problem of platoon starting. As the resistance of the vehicle during driving varies with time, this study regarded the CAV platoon system as a changing dynamic system and introduced the Udwadia–Kalaba (U–K) approach to simplify the solution. Apart from adding an equality constraint, unlike most other studies, this study imposed a bilateral inequality constraint on the intervehicle distance between successive CAVs to prevent collisions. Meanwhile, a diffeomorphism method was introduced to transform the bounded state into an unbounded state. The proposed control strategy could render each CAV compliant with both the original imposed bilateral inequality constraint and the equality constraint. The former avoids collisions, and the latter indicates the string stability of the designed CAV platoon system. The effectiveness of the proposed controller was verified by numerical experiments. The gap errors tend to converge to zero, which is not amplified by the propagation of traffic flow.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".